class_docstr: RoI Align module
  
  For more details, please refer to the document of roi_align in
  in https://github.com/PaddlePaddle/Paddle/blob/develop/python/paddle/vision/ops.py
  
  Args:
      resolution (int): The output size, default 14
      spatial_scale (float): Multiplicative spatial scale factor to translate
          ROI coords from their input scale to the scale used when pooling.
          default 0.0625
      sampling_ratio (int): The number of sampling points in the interpolation
          grid, default 0
      canconical_level (int): The referring level of FPN layer with 
          specified level. default 4
      canonical_size (int): The referring scale of FPN layer with 
          specified scale. default 224
      start_level (int): The start level of FPN layer to extract RoI feature,
          default 0
      end_level (int): The end level of FPN layer to extract RoI feature,
          default 3
      aligned (bool): Whether to add offset to rois' coord in roi_align.
          default false
code: def __init__(
      self,
      resolution=14,
      spatial_scale=0.0625,
      sampling_ratio=0,
      canconical_level=4,
      canonical_size=224,
      start_level=0,
      end_level=3,
      aligned=False,
  ):
      super(RoIAlign, self).__init__()
      self.resolution = resolution
      self.spatial_scale = _to_list(spatial_scale)
      self.sampling_ratio = sampling_ratio
      self.canconical_level = canconical_level
      self.canonical_size = canonical_size
      self.start_level = start_level
      self.end_level = end_level
      self.aligned = aligned
  
file_path: PaddleDetection/ppdet/modeling/heads/roi_extractor.py
imports: import paddle
  from ppdet.core.workspace import register
  from ppdet.modeling import ops
  import paddle.nn
label_desc: Use image node_id 1 to create a new RoIAlign object from inherited base classes: nn with example: obj = RoIAlign(resolution, spatial_scale, sampling_ratio, canconical_level, canonical_size, start_level, end_level, aligned)